91 citations · 178 across the 17 of their papers we have counts for
17 papers
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
Jonathan Hehir, Daniel Ting, Graham Cormode
Data sketching is a critical tool for distinct counting, enabling multisets to be represented by compact summaries that admit fast cardinality estimates. Because sketches may be me…
Order-Invariant Cardinality Estimators Are Differentially Private
Charlie Dickens, Justin Thaler, Daniel Ting
We consider privacy in the context of streaming algorithms for cardinality estimation. We show that a large class of algorithms all satisfy -differential privacy, so long as (a)…
Few-Shot Domain Adaptation with Polymorphic Transformers
Shaohua Li, Xiuchao Sui, Jie Fu +7
Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the train…
Simple, Optimal Algorithms for Random Sampling Without Replacement
Daniel Ting
Consider the fundamental problem of drawing a simple random sample of size k without replacement from [n] := {1, . . . , n}. Although a number of classical algorithms exist for thi…
HyperLogLog (HLL) Security: Inflating Cardinality Estimates
Pedro Reviriego, Pablo Adell, Daniel Ting
Counting the number of distinct elements on a set is needed in many applications, for example to track the number of unique users in Internet services or the number of distinct flo…
Manifold Learning via Manifold Deflation
Daniel Ting, Michael I. Jordan
Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on s…